COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer

10/16/2021
by   Juntao Jiang, et al.
0

The Coronavirus Disease 2019 (COVID-19) has spread globally and caused serious damages. Chest X-ray images are widely used for COVID-19 diagnosis and Artificial Intelligence method can assist to increase the efficiency and accuracy. In the Challenge of Chest XR COVID-19 detection in Ethics and Explainability for Responsible Data Science (EE-RDS) conference 2021, we proposed a method which combined Swin Transformer and Transformer in Transformer to classify chest X-ray images as three classes: COVID-19, Pneumonia and Normal (healthy) and achieved 0.9475 accuracy on test set.

READ FULL TEXT
research
07/16/2021

An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images

Artificial intelligence techniques are used on chest X-ray images for ac...
research
06/12/2023

Enhancing COVID-19 Diagnosis through Vision Transformer-Based Analysis of Chest X-ray Images

The advent of 2019 Coronavirus (COVID-19) has engendered a momentous glo...
research
10/09/2021

Vision Transformer based COVID-19 Detection using Chest X-rays

COVID-19 is a global pandemic, and detecting them is a momentous task fo...
research
04/11/2020

Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review

In 2019, the entire world is facing a situation of health emergency due ...
research
07/17/2023

Study of Vision Transformers for Covid-19 Detection from Chest X-rays

The COVID-19 pandemic has led to a global health crisis, highlighting th...
research
10/24/2021

Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data

There are multiple papers published about different AI models for the CO...
research
09/27/2022

CCTCOVID: COVID-19 Detection from Chest X-Ray Images Using Compact Convolutional Transformers

COVID-19 is a novel virus that attacks the upper respiratory tract and t...

Please sign up or login with your details

Forgot password? Click here to reset